The presently disclosed subject matter relates to methods and systems for the encoding and reconstruction of signals encoded with time encoding machines (TEM), and more particularly to the reconstruction of signals encoded with TEMs with the use of recurrent neural networks.
Most signals in the natural world are analog, i.e., they cover a continuous range of amplitude values. However, most computer systems for processing these signals are binary digital systems. Synchronous analog-to-digital (A/D) converters can be used to capture analog signals and present a digital approximation of the input signal to a computer processor. That is, at certain moments in time synchronized to a system clock, the amplitude of the signal of interest is captured as a digital value. When sampling the amplitude of an analog signal, each bit in the digital representation of the signal represents an increment of voltage, which defines the resolution of the A/D converter. Analog-to-digital conversion is used in many applications, such as communications where a signal to be communicated can be converted from an analog signal, such as voice, to a digital signal prior to transport along a transmission line.
Applying traditional sampling theory, a band limited signal can be represented with a quantifiable error by sampling the analog signal at a sampling rate at or above what is commonly referred to as the Nyquist sampling rate. It is a trend in electronic circuit design to reduce the available operating voltage provided to integrated circuit devices. In this regard, power supply voltages for circuits are generally decreasing. While digital signals can be processed at the lower supply voltages, traditional synchronous sampling of the amplitude of a signal becomes more difficult as the available power supply voltage is reduced and each bit in the A/D or D/A converter reflects a substantially lower voltage increment.
Time Encoding Machines (TEMs) can encode analog information in the time domain using only asynchronous circuits. Representation in the time domain can be an alternative to the classical sampling representation in the amplitude domain. Applications for TEMs can be found in low power nano-sensors for analog-to-discrete (A/D) conversion as well as in modeling olfactory systems, vision and audition in neuroscience.